Combretastatin A4 phosphate encapsulated in hyaluronic acid nanoparticles is highly cytotoxic to oral squamous cell carcinoma

Introduction To investigate the toxicity of combretastatin A4 phosphate (CA4P) hyaluronic acid (HA) gel nanoparticles (HA-CA4P-NPs) in OSCC (oral squamous cell carcinoma). Methods Toxicity was investigated using fluorescence microscopy, MTT as-say, flow cytometry, and OSCC xenograft mouse models. Results Compared with CA4P, HA-CA4P-NPs generated nearly 10 times more fluorescence in OSCC cells. Cytotoxicity assays showed that HACA4P-NPs were more toxic to SCC-4 cells but not to HNECs. Remarkable necrosis was induced in SCC-4 cells after exposure to HA-CA4P-NPs, and related proteins were upregulated. Furthermore, HA-CA4P-NPs significantly reduced the tu-mour size. Conclusions HA-CA4P-NPs improved drug release and delivery, and in-creased cytotoxicity to cancer cells

A Reduced-Order Successive Linear Estimator for Geostatistical Inversion and its Application in Hydraulic Tomography

Hydraulic tomography (HT) is a recently developed technology for characterizing high-resolution, site-specific heterogeneity using hydraulic data (n(d)) from a series of cross-hole pumping tests. To properly account for the subsurface heterogeneity and to flexibly incorporate additional information, geostatistical inverse models, which permit a large number of spatially correlated unknowns (n(y)), are frequently used to interpret the collected data. However, the memory storage requirements for the covariance of the unknowns (n(y) x n(y)) in these models are prodigious for large-scale 3-D problems. Moreover, the sensitivity evaluation is often computationally intensive using traditional difference method (n(y) forward runs). Although employment of the adjoint method can reduce the cost to n(d) forward runs, the adjoint model requires intrusive coding effort. In order to resolve these issues, this paper presents a Reduced-Order Successive Linear Estimator (ROSLE) for analyzing HT data. This new estimator approximates the covariance of the unknowns using Karhunen-Loeve Expansion (KLE) truncated to n(kl) order, and it calculates the directional sensitivities (in the directions of n(kl) eigenvectors) to form the covariance and cross-covariance used in the Successive Linear Estimator (SLE). In addition, the covariance of unknowns is updated every iteration by updating the eigenvalues and eigenfunctions. The computational advantages of the proposed algorithm are demonstrated through numerical experiments and a 3-D transient HT analysis of data from a highly heterogeneous field site.National Natural Science Foundation of China [51779179, 51609173, 51479144, 51522904]; CRDF [DAA2-15-61224-1]; Tianjin Normal University from the Thousand Talents Plan of Tianjin City; Special Fund for Public Industry Research from Ministry of Land and Resources of China [201511047]6 month embargo; published online: 16 February 2018This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]